A rapid, simple, sensitive, and selective point-of-care diagnosis tool kit is vital for detecting the coronavirus disease (COVID-19) based on the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strain. Currently, the reverse transcriptase-polymerase chain reaction (RT-PCR) is the best technique to detect the disease. Although a good sensitivity has been observed in RT-PCR, the isolation and screening process for high sample volume is limited due to the time-consuming and laborious work. This study introduced a nucleic acid-based surface-enhanced Raman scattering (SERS) sensor to detect the nucleocapsid gene (N-gene) of SARS-CoV-2. The Raman scattering signal was amplified using gold nanoparticles (AuNPs) possessing a rod-like morphology to improve the SERS effect, which was approximately 12-15 nm in diameter and 40-50 nm in length. These nanoparticles were functionalised with the single-stranded deoxyribonucleic acid (ssDNA) complemented with the N-gene. Furthermore, the study demonstrates method selectivity by strategically testing the same virus genome at different locations. This focused approach showcases the method's capability to discern specific genetic variations, ensuring accuracy in viral detection. A multivariate statistical analysis technique was then applied to analyse the raw SERS spectra data using the principal component analysis (PCA). An acceptable variance amount was demonstrated by the overall variance (82.4 %) for PC1 and PC2, which exceeded the desired value of 80 %. These results successfully revealed the hidden information in the raw SERS spectra data. The outcome suggested a more significant thymine base detection than other nitrogenous bases at wavenumbers 613, 779, 1219, 1345, and 1382 cm-1. Adenine was also less observed at 734 cm-1, and ssDNA-RNA hybridisations were presented in the ketone with amino base SERS bands in 1746, 1815, 1871, and 1971 cm-1 of the fingerprint. Overall, the N-gene could be detected as low as 0.1 nM within 10 mins of incubation time. This approach could be developed as an alternative point-of-care diagnosis tool kit to detect and monitor the COVID-19 disease.
Dental caries has high prevalence among kids and adults thus it has become one of the global health concerns. The current modern dentistry focused on the preventives measures to reduce the number of dental caries cases. The employment of machine learning coupled with UV spectroscopy plays a crucial role to detect the early stage of caries. Artificial neural network with hyperparameter tuning was employed to train spectral data for the classification based on the International Caries Detection and Assesment System (ICDAS). Spectra preprocessing namely mean center (MC), autoscale (AS) and Savitzky Golay smoothing (SG) were applied on the data for spectra correction. The best performance of ANN model obtained has accuracy of 0.85 with precision of 1.00. Convolutional neural network (CNN) combined with Savitzky Golay smoothing performed on the spectral data has accuracy, precision, sensitivity and specificity for validation data of 1.00 respectively. The result obtained shows that the application of ANN and CNN capable to produce robust model to be used as an early screening of dental caries.
Binding mechanisms of two selected pesticides, propazine (PRO) and quinoxyfen (QUI) with bovine serum albumin (BSA) was examined using fluorescence, absorption and molecular docking methods. Intrinsic fluorescence of BSA was quenched in the presence of both PRO and QUI. The quenching was ascertained to be conversely linked to temperature, which suggested the contribution of static quenching process in the PRO-BSA and QUI-BSA complex formations. This results were validated by the enhancement in absorption spectrum of BSA upon binding with PRO and QUI. Binding constant values (Kf = 9.55-0.60 × 10-3 M-1 for PRO-BSA system; Kf = 7.08-5.01 × 102 M-1 for QUI-BSA system) and number of binding site (n) values for the PRO-BSA and QUI-BSA systems at different temperatures affirmed a weak binding strength with a set of equivalent binding sites on BSA. Thermodynamic data obtained for both the PRO-BSA and QUI-BSA interactions predicted that the association process was spontaneous and non-covalent contacts such as hydrophobic interactions, van der Waals forces and hydrogen bonds participated in the binding reactions. This result was further supported by the molecular docking assessments. Three-dimensional spectral results revealed the microenvironmental alterations near tryptophan (Trp) and tyrosine (Tyr) residues in BSA by the addition of PRO and QUI. The docking analysis demonstrated the binding pattern for the PRO-BSA and QUI-BSA systems and disclosed the preferred binding site of both PRO and QUI as site I (subdomain IIA) of BSA.
Utilizing visible and near-infrared (Vis-NIR) spectroscopy in conjunction with chemometrics methods has been widespread for identifying plant diseases. However, a key obstacle involves the extraction of relevant spectral characteristics. This study aimed to enhance sugarcane disease recognition by combining convolutional neural network (CNN) with continuous wavelet transform (CWT) spectrograms for spectral features extraction within the Vis-NIR spectra (380-1400 nm) to improve the accuracy of sugarcane diseases recognition. Using 130 sugarcane leaf samples, the obtained one-dimensional CWT coefficients from Vis-NIR spectra were transformed into two-dimensional spectrograms. Employing CNN, spectrogram features were extracted and incorporated into decision tree, K-nearest neighbour, partial least squares discriminant analysis, and random forest (RF) calibration models. The RF model, integrating spectrogram-derived features, demonstrated the best performance with an average precision of 0.9111, sensitivity of 0.9733, specificity of 0.9791, and accuracy of 0.9487. This study may offer a non-destructive, rapid, and accurate means to detect sugarcane diseases, enabling farmers to receive timely and actionable insights on the crops' health, thus minimizing crop loss and optimizing yields.
Wavelength selection is crucial to the success of near-infrared (NIR) spectroscopy analysis as it considerably improves the generalization of the multivariate model and reduces model complexity. This study proposes a new wavelength selection method, interval flower pollination algorithm (iFPA), for spectral variable selection in the partial least squares regression (PLSR) model. The proposed iFPA consists of three phases. First, the flower pollination algorithm is applied to search for informative spectral variables, followed by variable elimination. Subsequently, the iFPA performs a local search to determine the best continuous interval spectral variables. The interpretability of the selected variables is assessed on three public NIR datasets (corn, diesel and soil datasets). Performance comparison with other competing wavelength selection methods shows that the iFPA used in conjunction with the PLSR model gives better prediction performance, with the root mean square error of prediction values of 0.0096-0.0727, 0.0015-3.9717 and 1.3388-29.1144 are obtained for various responses in corn, diesel and soil datasets, respectively.
The proliferation of pathogenic fungi in sugarcane crops poses a significant threat to agricultural productivity and economic sustainability. Early identification and management of sugarcane diseases are therefore crucial to mitigate the adverse impacts of these pathogens. In this study, visible and near-infrared spectroscopy (380-1400 nm) combined with a novel wavelength selection method, referred to as modified flower pollination algorithm (MFPA), was utilized for sugarcane disease recognition. The selected wavelengths were incorporated into machine learning models, including Naïve Bayes, random forest, and support vector machine (SVM). The developed simplified SVM model, which utilized the MFPA wavelength selection method yielded the best performances, achieving a precision value of 0.9753, a sensitivity value of 0.9259, a specificity value of 0.9524, and an accuracy of 0.9487. These results outperformed those obtained by other wavelength selection approaches, including the selectivity ratio, variable importance in projection, and the baseline method of the flower pollination algorithm.
Consumption of agricultural products with pesticide residue is risky and can negatively affect health. This study proposed a nondestructive method of detecting pesticide residues in chili pepper based on the combination of visible and near-infrared (VIS/NIR) spectroscopy (400-2498 nm) and deep learning modeling. The obtained spectra of chili peppers with two types of pesticide residues (acetamiprid and imidacloprid) were analyzed using a one-dimensional convolutional neural network (1D-CNN). Compared with the commonly used partial least squares regression model, the 1D-CNN approach yielded higher prediction accuracy, with a root mean square error of calibration of 0.23 and 0.28 mg/kg and a root mean square error of prediction of 0.55 and 0.49 mg/kg for the acetamiprid and imidacloprid data sets, respectively. Overall, the results indicate that the combination of the 1D-CNN model and VIS/NIR spectroscopy is a promising nondestructive method of identifying pesticide residues in chili pepper.
Dioscorea oppositifolia is an important crop and functional food. D. oppositifolia tuber is often adulterated with D. persimilis, D. alata, and D. fordii tuber in the commercial market. This study proposed an integrated Fourier transform infrared spectroscopy (FT-IR) with chemometric approach to differentiate these four Dioscorea species. A total of 107 Dioscorea spp. tuber samples were collected from different locations in China. Principal Component Analysis (PCA), PCA-Class, and Orthogonal Partial Least Square Discriminant Analysis (OPLS-DA) were utilised to classify the FT-IR spectra. In this PCA is unable to differentiate the Dioscorea spp. tuber effectively. However, PCA-Class and OPLS-DA can distinguish spp. these 4 species Dioscorea tuber with high accuracy, sensitivity, and specificity. Additionally, the RMSEE, RMSEP and RMSECV values for OPLS-DA model were low, showing that it is a good model. The combination of FT-IR with the PCA-Class and OPLS-DA is practical in discriminating Dioscorea spp. tubers.
A new thiosemicarbazone derivative, N-(2-hydroxyphenyl)-2-[1-(pyridin-4-yl)ethylidene]hydrazinecarbothioamide (HPEH), has been synthesized, characterized, and further developed as a highly selective and sensitive colorimetric chemosensor for Hg2+ recognition in environmental water samples. Structural conformers of HPEH were successfully identified using a combination of the potential energy surface (PES) and time-dependent density functional theory (TD-DFT) methods. The synthesized HPEH was successfully characterized further and analyzed based on its harmonic vibrational frequencies, NMR spectra, and electronic transitions using the DFT approach. Sigma profiles were generated using the COSMO-RS approach to identify a compatible medium for HPEH to act as a chemosensor. The conditions for the highly sensitive and selective detection of Hg2+ by HPEH were successfully optimized using the statistical response surface methodology approach. The optimum sensing of HPEH occurred in an 8:2 v/v DMSO/pH 7.8 solution at a 20:60 μM HPEH/Hg2+ concentration and after a reaction time of 18 min, with statistically significant independent variables (p
Experiments demonstrated that visible and near-infrared (Vis-NIR) spectroscopy is a highly reliable tool for determining the nutritional status of plants. Although numerous studies on various kinds of plants have been conducted, there are only a few summaries of the research findings regarding the absorbance bands in the visible and near-infrared region and how they relate to the nutritional status of plants. This article will discuss the application of Vis-NIR spectroscopy for monitoring the nutrient conditions of plants, with a particular emphasis on three major components required by plants, namely nitrogen (N), phosphorus (P), and potassium (K), or NPK. Each section discussed different topics, for instance, the essential nutrients needed by plants, the application of Vis-NIR spectroscopy in nutrient status analysis, chemometrics tools, and absorbance bands related to the nutrient status, respectively. Deduction made concluded that factors affecting the plant's structure are contributed by several circumstances like the age of leaves, concentration of pigments, and water content. These factors are intertwined, strongly correlated, and can be observed in the visible and near-infrared regions. While the visible region is commonly utilised for nutritional analysis in plants, the literature review performed in this paper shows that the near-infrared region as well contains valuable information about the plant's nutritional status. A few wavelengths related to the direct estimation of nutrients in this review explained that information on nutrients can be linked with chlorophyll and water absorption bands such that N and P are the components of chlorophyll and protein; on the other hand, K exists in the form of cationic carbohydrates which are sensitive to water region.
Accurate, label-free, and rapid methods for measuring phosphorus concentrations are essential in a hydroponic system, as excessive or insufficient phosphorus levels can adversely affect plant growth, human health, and environmental sustainability. In this study, we demonstrate the advantages of hybrid machine learning models compared to single machine learning models in predicting phosphorus concentration based on the absorbance dataset. Three machine learning classifiers- Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)- were employed as bases for single and hybrid machine learning models. Three ensemble techniques (voting, bagging, and stacking) were used to hybridize the classifiers. Among the single models, KNN demonstrated the fastest computational time of 18.07 s, while SVM achieved the highest accuracy of 99.6%. The hybrid SVM/KNN model using a voting classifier showed a significant increase in accuracy for KNN with only a slight increase in computational time. Bagging techniques increased the accuracy but at a longer computational time. The stacking technique, which combined SVM, KNN, and RF, achieved the highest accuracy of 99.73% with a short computational time of 36.18 s compared to the bagging and voting technique. This study demonstrates that the machine learning method can effectively distinguish phosphorus concentrations. In contrast, hybrid machine learning techniques can improve accuracy for predicting phosphorus without using labels, despite requiring longer computational time.
Spectroscopy in the visible and near-infrared region (Vis-NIR) region has proven to be an effective technique for quantifying the chlorophyll contents of plants, which serves as an important indicator of their photosynthetic rate and health status. However, the Vis-NIR spectroscopy analysis confronts a significant challenge concerning the existence of spectral variations and interferences induced by diverse factors. Hence, the selection of characteristic wavelengths plays a crucial role in Vis-NIR spectroscopy analysis. In this study, a novel wavelength selection approach known as the modified regression coefficient (MRC) selection method was introduced to enhance the diagnostic accuracy of chlorophyll content in sugarcane leaves. Experimental data comprising spectral reflectance measurements (220-1400 nm) were collected from sugarcane leaf samples at different growth stages, including seedling, tillering, and jointing, and the corresponding chlorophyll contents were measured. The proposed MRC method was employed to select optimal wavelengths for analysis, and subsequent partial least squares regression (PLSR) and Gaussian process regression (GPR) models were developed to establish the relationship between the selected wavelengths and the measured chlorophyll contents. In comparison to full-spectrum modelling and other commonly employed wavelength selection techniques, the proposed simplified MRC-GPR model, utilizing a subset of 291 selected wavelengths, demonstrated superior performance. The MRC-GPR model achieved higher coefficient of determination of 0.9665 and 0.8659, and lower root mean squared error of 1.7624 and 3.2029, for calibration set and prediction set, respectively. Results showed that the GPR model, a nonlinear regression approach, outperformed the PLSR model.
The fabrication of molecular electronics from non-toxic functional materials which eventually would potentially able to degrade or being breaking down into safe by-products have attracted much interests in recent years. Hence, in this study, the introduction of mixed highly functional substructures of chalcone (-CO-CH=CH-) and ethynylated (C≡C) as building blocks has shown ideal performance as solution-processed thin film candidatures. Two types of derivatives, (MM-3a) and (MM-3b) repectively, showed a substantial Stokes shifts at 75 nm and 116 nm, in which such emission exhibits an intramolecular charge transfer (ICT) state and fluoresce characteristics. The density functional theory (DFT) simulation shows that MM-3a and MM-3b exhibit low energy gaps of 3.70 eV and 2.81 eV, respectively. TD-DFT computations for molecular electrostatic potential (MEP) and frontier molecular orbitals (FMO) were also used to emphasise the structure-property relationship. A solution-processed thin film with a single layer of ITO/PEDOT:PSS/MM-3a-MM-3b/Au exhibited electroluminescence behaviour with orange and purple emissions when supplied with direct current (DC) voltages. To promote the safer application of the derivatives formed, ethynylated chalcone materials underwent toxicity studies toward Acanthamoeba sp. to determine their suitability as non-toxic molecules prior to the determination as safer materials in optical limiting interests. From the preliminary test, no IC50 value was obtained for both compounds via 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay analysis and molecular docking analysis between MM-3a and MM-3b, with profilin protein exhibited weak bond interactions and attaining huge interaction distances.
Trace and minor elements play crucial roles in a variety of biological processes, including amyloid fibrils formation. Mechanisms include activation or inhibition of enzymatic reactions, competition between elements and metal proteins for binding positions, also changes to the permeability of cellular membranes. These may influence carcinogenic processes, with trace and minor element concentrations in normal and amyloid tissues potentially aiding in cancer diagnosis and etiology. With the analytical capability of the spectroscopic technique X-ray fluorescence (XRF), this can be used to detect and quantify the presence of elements in amyloid characterization, two of the trace elements known to be associated with amyloid fibrils. In present work, involving samples from a total of 22 subjects, samples of normal and amyloid-containing tissues of heart, kidney, thyroid, and other tissue organs were obtained, analyzed via energy-dispersive X-ray fluorescence (EDXRF). The elemental distribution of potassium (K), calcium (Ca), arsenic (As), and iron (Fe) was examined in both normal and amyloidogenic tissues using perpetual thin slices. In amyloidogenic tissues the levels of K, Ca, and Fe were found to be less than in corresponding normal tissues. Moreover, the presence of As was only observed in amyloidogenic samples; in a few cases in which there was an absence of As, amyloid samples were found to contain Fe. Analysis of arsenic in amyloid plaques has previously been difficult, often producing contradictory results. Using the present EDXRF facility we could distinguish between amyloidogenic and normal samples, with potential correlations in respect of the presence or concentration of specific elements.
The binding mode of antineoplastic antimetabolite, floxuridine (FUDR), with human serum albumin (HSA), the leading carrier in blood circulation, was ascertained using multi-spectroscopic, microscopic, and computational techniques. A static fluorescence quenching was established due to decreased Ksv values with rising temperatures, suggesting FUDR-HSA complexation. UV-vis absorption spectral results also supported this conclusion. The binding constant, Ka values, were found within 9.7-7.9 × 103 M-1 at 290, 300, and 310 K, demonstrating a moderate binding affinity for the FUDR-HSA system. Thermodynamic data (ΔS = +46.35 J.mol-1.K-1 and ΔH = -8.77 kJ.mol-1) predicted the nature of stabilizing forces (hydrogen-bonds, hydrophobic, and van der Waals interactions) for the FUDR-HSA complex. Circular dichroism spectra displayed a minor disruption in the protein's 2° and 3° structures. At the same time, atomic force microscopy images proved variations in the FUDR-HSA surface morphology, confirming its complex formation. The protein's microenvironment around Trp/Tyr residues was also modified, as judged by 3-D fluorescence spectra. FUDR-bound HSA showed better resistance against thermal stress. As disclosed from ligand displacement studies, the FUDR binding site was placed in subdomain IIA (Site I). Further, the molecular docking analysis corroborated the competing displacement studies. Molecular dynamics evaluations revealed that the complex achieved equilibrium during simulations, confirming the FUDR-HSA complex's stability.
Spaceflight represents a complex environmental condition. Space mutagenesis breeding has achieved and marked certain results over the years. This method was employed in our previous studies in order to obtain improved germplasm of Isatis indigotica. This study is to determine the chemical changes in I. indigotica seeds carried after Chinese first spaceship (Shenzhou I). Fourier transform infrared (FTIR), second derivative and two-dimensional infrared (2DIR) correlation spectroscopy were used in analysis. Not much differences between the two spectra were found except the peaks in the range of 1500-1200 cm(-)(1) which was about 7 cm(-)(1) different and indicated the absorption could be initialed from different bonds. SP4 showed different derivative compared with C4 in the second derivative spectra of 1200-800 cm(-)(1). The stronger signal of 2DIR in SP4 indicated the protein content of the seed was changed after spaceflight. It is concluded that spaceflight provided an extreme condition that caused changes of chemical properties in I. indigotica.
In this study, mannitol-functionalized magnetic nanoparticles (MMNPs) as a unique nanosorbent and N-doped fluorescent carbon dots (N-CDs) as a cost-effective nanosensor were created and utilized, for the first time, for dispersive micro-solid-phase extraction (Dµ-SPE) to determine carmine (E120) dye in water samples and juices. The modification of the magnetic nanoparticles with mannitol was designed to enhance the responsive potential for adsorption of the polar E120 dye from complex sample matrices through electrostatic interaction. The as-fabricated N-CDs fluorescent probe exhibited a high fluorescence quantum yield (Φs) of 43.1 %, allowing for accurate fluorometric detection of E120 dye. The as-synthesized MMNPs nanosorbent and fluorescent N-CDs nanoprobe were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), energy dispersive X-ray (EDX), thermogravimetric analysis (TGA), and vibrating-sample magnetometer (VSM). Density functional theory (DFT) studied the E120 dye structure using Gaussian 09 to explore the interactions between E 120 dye molecules and MMNPs/N-CDs. The impact of the critical adsorption and detection experimental factors was investigated and adjusted. A minimal amount of MMNPs nanosorbent (150 mg) is sufficient for E120 extraction in an acceptable time of 15 min. Furthermore, with a high determination coefficient, the adsorption characteristics fit with the models of Langmuir isotherm and first-order kinetics. The maximum adsorption capacity (qm) of the as-fabricated MMNPs was 87.7 mg.g-1. After adsorption, E120 dye was fluorometrically analyzed using nitrogen-doped carbon dots as a fluorescent nanosensor via the inner filter effect (IFE) mechanism. Under the optimized conditions, the proposed fluorometric procedures showed a linear increase in the fluorescence ratio with increasing the E120 concentration in the range of 1.0 - 160.0 μg.mL-1 with detection (LOD) and quantitation (LOQ) limits of 0.27 and 0.83 μg.mL-1, respectively. The relative standard deviation (%RSD) did not exceed 2.34 %. The proposed methodology was successfully applied to determine E120 dye in juice and environmental water samples with % recovery ranged from 89.2-106.1 % and 92.9-107.2 %, respectively offering a reliable and environmentally friendly alternative to traditional detection methods with potential applications across various industries.
The development of a new Neodymium/Dysprosium metal-organic framework, referred to as Nd/Dy-BTC MOF, based on benzene-1,2,4-tricarboxylic acid, has been achieved through an in situ growth process on 2D transition metal carbides (MXene) surfaces. This synthesis was conducted via a solvothermal method utilizing a solvent mixture of water, ethanol, and dimethylformamide. The primary objective of this research is to investigate the framework's efficacy as a photocatalyst for the degradation of anionic dye, as well as its potential for sensing certain explosives. The Nd/Dy-MOF@MXene has been characterized by various analysis techniques. The prepared Nd/Dy-MOF@MXene was utilized as catalyst in selective degradation of methyl orange dye. The study examined the influence of pH and catalyst concentration, revealing that the catalyst achieves peak efficiency of 93.55% when exposed to sunlight in an acidic medium. The reusability of the catalyst shows the highest efficient photocatalyst after four cycles of reusability. The detection of explosives, specifically 2,4,6-trinitrophenol, through photoluminescence techniques has been conducted, utilizing the Stern-Volmer equation to evaluate the quenching efficiency. The findings indicated remarkable efficiency and selectivity of Nd/Dy-MOF@MXene for 2,4,6-trinitrophenol, achieving a quenching efficiency of 91%. Furthermore, the reusability tests demonstrated that Nd/Dy-MOF@MXene exhibits outstanding recyclability, maintaining performance over five cycles. The antibacterial activity of Nd/Dy-MOF@MXene was investigated against Gram-positive and Gram-negative strains due to indication of medicine properties of Nd/Dy-MOF@MXene. These results paves a way for manufacturing innnovation in near future.
Light-matter interaction has been profoundly studied for sample material classification. However, the optical classification of the sample through the polarized light-matter interaction remains underexplored. It is limited to the measurement of intensity instead of the angle of polarized light (AOP) for its degree of polarization. Measurement of the degree of polarization within a material or a medium becomes easier with a simple, low-cost and direct measurement without the need of any probing or labelling agent. Thus, this investigation was conducted mainly to determine the angle of polarized light (AOP) property of the crosslinked polymer using our proposed polarization measurement technique as an alternative approach of the material classification. The angle of polarized light (AOP) of each polymer was determined in combination property of polarization by absorption, transmission, and scattering. Our proposed scattered angle (ס=90°, 100°, 110°, and 120°) successfully measured the AOP of each polymer that can be classified into two groups. Group 1 represents the AOP value ( [Formula: see text] ) for a test sample of t1 = 3.1 %, 3.2, and 3.3 % with comparison to the normal sample (n = 3.0 %) and Group 2 represents the AOP value ( [Formula: see text] ) for the test sample oft2 = 3.4 %, 3.6 % and 3.7 % with comparison to the normal sample (n = 3.0 %). Our study proved a direct, easy, and simple method of determining the degree of polarization of the polymers without the need of complex formulation and labelling protocol. Therefore, this work may enhance the investigation of the optical properties of the agarose-based tissue-mimicking phantom (AGTMP) for modeling or simulation of the real biological sample in the future. Our polarization measures are worthy of further explored and implemented in current optical imaging techniques or sensing platform for optical classification of the biomaterials.
This study investigates the photophysical and electrical properties of 4-heptamethyl-trisiloxanyl-n-undecyloxy-4'-nitrostilbene (SNS), a low molar mass organosiloxane liquid crystal containing a nitrostilbene chromophore. Real-time monitoring of the absorption and fluorescence spectra of the nitrostilbene moiety was conducted in dichloromethane solution and thin films (in both crystalline and Smectic C (SmC) phases). A charge transfer excited state is formed following vibrational cooling and solvent interaction within 0.4 to 1.6 ps, which then relaxes to the ground state in 1.6 ns. Five distinct lifetime components were identified in thin films, attributed to the heterogeneous local environment and aggregate formation. Notable differences in excited-state lifetimes were observed between the crystalline and SmC phases, with slower dynamics in the former due to the rigidity of the crystalline phase. Photoconductivity under UV irradiation was examined in SmC, Smectic A (SmA), and isotropic phases, showing a significant increase in current response, particularly in the SmC phase. Polarizing optical microscopy revealed morphological changes post-UV exposure, such as reduced SmC domain size and decreased birefringence. Dielectric measurements indicated distinct relaxation peaks in SmC and SmA phases, reflecting more disordered molecular arrangements. The study reveals that UV exposure significantly enhances SNS conductivity, particularly within the SmC phase. This enhancement is likely attributed to UV excitation promoting the formation of an intramolecular charge transfer state within the trans-stilbene moiety. These findings provide valuable insights into the potential applications of nitrostilbene-functionalized organosiloxane liquid crystals in optoelectronic devices, such as light switches and photodetectors.